This proposal seeks support for our studies on the effect of spinal cord injury on male reproduction. Although anejaculation is a relatively uncommon occurrence in the general population, over 12,000 new cases are reported annually. The major cause of ejaculatory dysfunction is spinal cord injury, usually occurring as a result of a motor vehicle accident. Infertility in spinal cord injured men is reported to approach 95%. This impaired fertility is thought to be due to anejaculation secondary to neuromuscular dysfunction, obstruction of the genital passages secondary to infections and/or impaired spermatogenesis. Electroejaculation, a procedure for inducing the seminal fluid emission by electrically stimulating specific nerves of the male reproductive tract with rectal probe electrostimulation, has been used to obtain sperm from these patients. Nevertheless, poor sperm quality has been a consistent finding and pregnancy rates remain low. Although adequate sperm densities usually can be obtained, the low sperm motility appears to be a limiting factor. The information on variables that correlate with semen quality and ultimate pregnancy success or failure is far from complete. We have proposed to develop a standardized protocol to be used in a large multi-- institutional study of electroejaculation (EEJ) of SCI men to correlate patient evaluation, treatment and data collection. We will evaluate the epididymal secretion of specific proteins, as well as the hormonal and spermatogenic characteristics of a large population of spinal cord injured men. Changes in genital tract protein secretion after neurologic injury will provide the potential to develop a useful prognostic assay for damage to the proximal ductal system. Most significantly we have proposed to devise a new approach for the multivariate analysis of the data using an artificial neural network Traditional statistical analysis of fertility has proven very unsatisfactory, with life table analysis commonly employed as a means of approximating the evaluation of fertility potential. By "learning" and "generalizing", the neural network model provides an ideal method of addressing such a complex issue. Upon completion of these aims we will have performed the largest study to date on the effect of SCI on male reproductive function and will have used this information to develop a powerful new system for the diagnosis of fertility potential in this unique patient population.